The present invention relates to adaptive antenna arrays and methods for controlling adaptive antenna arrays, and, in particular, to adaptive antenna arrays using non-data (e.g., pilot) symbol-assisted channel estimation and methods for controlling such arrays.
Given the proliferation of wireless communications devices in recent years, the wireless communications industry is now facing a significant and complex problem in terms of signal interference. Each mobile station, such as a cellular telephone, represents a potential source of interference for other mobile stations. Moreover, given recent trends in telecommunication use, the number of cellular telephones and other wireless communications devices will probably only increase over the next several years.
To limit the effects of the interference caused by the proliferation of wireless devices, an adaptive antenna array, also referred to as a smart antenna, having interference rejecting or nulling capabilities may be used. For example, the adaptive antenna array may perform a process referred to as diversity combining to compensate for interference as well as for signal fading.
After a mobile station transmits a radio frequency communication signal, the original signal encounters a variety of physical surfaces that cause the signal to be reflected. Because of these reflective surfaces, referred to as reflectors or scatterers, the original signal becomes a plurality of reflected signals. Each of these reflected signals arrives separately at the receiving device (for example, a base site having an adaptive antenna array including a plurality of antenna elements) after traveling along one of a multiplicity of paths. At each antenna element, the reflected signals combine together to form a composite signal.
The resultant propagation, or fading, characteristics of the composite signal differ from antenna element to antenna element due to spatial separation and/or the use of different polarizations. In diversity combining, the received signals are processed separately, weighted (e.g. multiplied by a weight) and then combined. The weights are calculated to compensate for interference and signal fading.
The weights may be calculated according to a variety of algorithms. Two such combining algorithms, the Least-Mean-Square (xe2x80x9cLMSxe2x80x9d) algorithm and the Direct Matrix Inversion (xe2x80x9cDMIxe2x80x9d) algorithm, are described by J. H. Winters, Signal Acquisition and Tracking with Adaptive Arrays in the Digital Mobile Radio System IS-54 with Flat Fading, IEEE Transactions on Vehicular Technology (November 1993). The LMS and DMI algorithms are designed to minimize the mean-square error between a reference signal and the processed, weighted, and combined output of the adaptive antenna array.
These algorithms are not without their disadvantages. As Winters points out, the LMS algorithm is not well suited for handling a signal interference phenomenon known as multipath fading, wherein the various signals received at the elements of the adaptive antenna array are out of phase with one another. Winters also admits that the DMI algorithm is also not well suited for use with time-varying signals, and recommends incorporating periodic xe2x80x9ctrainingxe2x80x9d or recalibration with such techniques as sliding windows or forgetting functions to compensate for the time varying nature of the signal with which the DMI algorithm is used.